V. Paszto, L. Marek, S. Hartmannova
Monday 5 August 2013 by Libadmin2013

References: 13th SGEM GeoConference on Informatics, Geoinformatics And Remote Sensing, www.sgem.org, SGEM2013 Conference Proceedings, ISBN 978-954-91818-9-0 / ISSN 1314-2704, June 16-22, 2013, Vol. 1, 313 - 320 pp


Since shape metrics emerged in the landscape ecology as a new tool for quantitative evaluation of a landscape, it has become easier for geocomputation methods in GIS to adopt theirs principles. Shape metrics tools are nowadays implemented in both GIS and stand-alone software. There are more than 100 shape metrics available to calculate shape quantitative properties. Nevertheless, there are still different scientific opinions about the usefulness of shape metrics.
Landscape ecology defines elementary, further indivisible unit of the landscape called patch. Every single patch is characterized by its shape (quantitative information) and represents a land cover type (qualitative information). This patch is formed by natural or artificial processes that determine shape itself. Therefore it is possible to study underlying processes only by analyzing the quantitative characteristics of the patch shape.
The main goal of the paper was to analyze urban footprint of major cities of European Union member states using shape metrics calculations. A city represents a specific group of patches, which is an example of socio-economic (artificial) evolution of the patch. There are various aspects that affected the urban area development, such as surrounding natural limits (mountains, rivers, seas etc.) or man-made limits (urban planning, road infrastructure, urban sprawl etc.) and thus the city shape vary throughout the dataset.
In the paper, only a geometric part of the data was processed and qualitative or attribute information was excluded. More than twenty different shape metrics were computed to get numerical characteristics of cities sample in order to find similar groups of the cities using cluster analysis.

Keywords: GIS, Shape Metrics, Geocomputation, Urban Areas, Clustering

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